
import geopandas as gpd
import matplotlib.pyplot as plt
#import folium
import folium
from folium import plugins
from folium.plugins import MeasureControl

import pandas as pd
import requests
import numpy as np
import pdvega # adds vgplot attribute to pandas
from vega import VegaLite

import json
import altair as alt


from altair import Chart, load_dataset

# load built-in dataset as a pandas DataFrame
cars = load_dataset("cars")
print(type(cars))

scatter = (
    Chart(cars)
    .mark_circle()
    .encode(
        x="Horsepower",
        y="Miles_per_Gallon",
        color="Origin",
    )
)

vega = folium.features.VegaLite(
    scatter,
    width="100%",
    height="100%",
)

m = folium.Map(location=[-27.5717, -48.6256]) #buat map dasar

marker = folium.features.Marker([-27.57, -48.62]) #buat marker

popup = folium.Popup() #buat wadah popup

vega.add_to(popup) #tambahkan chart vega ke popup
popup.add_to(marker) #tambahkan popup ke marker

marker.add_to(m)

plugins.LocateControl().add_to(m)

m

C:\Users\Gian Amanda\anaconda3\envs\geopandas_env\lib\site-packages\altair\utils\deprecation.py:65: AltairDeprecationWarning: load_dataset is deprecated. Use the vega_datasets package instead.
  warnings.warn(message, AltairDeprecationWarning)

<class 'pandas.core.frame.DataFrame'>


bandarlampung = gpd.read_file('Geospatial/bandarlampung/ADMINISTRASIKECAMATAN_AR_50K.shp')
#melihat data teratas
bandarlampung.head()


#create geodataframe
bandarlampung_gdf = gpd.GeoDataFrame(bandarlampung)
bandarlampung_gdf


#melihat data terbawah
bandarlampung.tail()


#melihat struktur geodataframe
bandarlampung.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 31 entries, 0 to 30
Data columns (total 28 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   KDPPUM      0 non-null      object  
 1   NAMOBJ      31 non-null     object  
 2   REMARK      31 non-null     object  
 3   KDPBPS      0 non-null      object  
 4   FCODE       0 non-null      object  
 5   LUASWH      31 non-null     float64 
 6   UUPP        0 non-null      object  
 7   SRS_ID      0 non-null      object  
 8   LCODE       31 non-null     object  
 9   METADATA    0 non-null      object  
 10  KDEBPS      0 non-null      object  
 11  KDEPUM      0 non-null      object  
 12  KDCBPS      0 non-null      object  
 13  KDCPUM      0 non-null      object  
 14  KDBBPS      0 non-null      object  
 15  KDBPUM      0 non-null      object  
 16  WADMKD      0 non-null      object  
 17  WIADKD      0 non-null      object  
 18  WADMKC      0 non-null      object  
 19  WIADKC      0 non-null      object  
 20  WADMKK      31 non-null     object  
 21  WIADKK      0 non-null      object  
 22  WADMPR      31 non-null     object  
 23  WIADPR      0 non-null      object  
 24  TIPADM      31 non-null     int64   
 25  Shape_Leng  31 non-null     float64 
 26  Shape_Area  31 non-null     float64 
 27  geometry    31 non-null     geometry
dtypes: float64(3), geometry(1), int64(1), object(23)
memory usage: 6.9+ KB


#manampilkan summary statistic
bandarlampung.describe()


#investigasi duplikasi nama data data non-numerik
bandarlampung['NAMOBJ'].value_counts()

PADANGCERMIN           3
TELUKBETUNG TIMUR      2
JATIAGUNG              1
TANJUNGBINTANG         1
RAJABASA               1
GEDONGTATAAN           1
TANJUNGSENANG          1
LABUHANRATU            1
SUKARAME               1
KEMILING               1
WAYHALIM               1
LANGKAPURA             1
KEDATON                1
TANJUNGKARANG BARAT    1
WAYLIMA                1
KEDAMAIAN              1
MERBAUMATARAM          1
SUKABUMI               1
TANJUNGKARANG TIMUR    1
TANJUNGKARANG PUSAT    1
ENGGAL                 1
TELUKBETUNG UTARA      1
TELUKBETUNG SELATAN    1
PANJANG                1
TELUKBETUNG BARAT      1
BUMIWARAS              1
KATIBUNG               1
NATAR                  1
Name: NAMOBJ, dtype: int64


#mengetahui luas area terbesar
bandarlampung.loc[bandarlampung['Shape_Area'].idxmax()]#luas area terbesar

KDPPUM                                                     None
NAMOBJ                                             PADANGCERMIN
REMARK                           Wilayah Administrasi Kecamatan
KDPBPS                                                     None
FCODE                                                      None
LUASWH                                                      0.0
UUPP                                                       None
SRS_ID                                                     None
LCODE                                                    BA0080
METADATA                                                   None
KDEBPS                                                     None
KDEPUM                                                     None
KDCBPS                                                     None
KDCPUM                                                     None
KDBBPS                                                     None
KDBPUM                                                     None
WADMKD                                                     None
WIADKD                                                     None
WADMKC                                                     None
WIADKC                                                     None
WADMKK                                                PESAWARAN
WIADKK                                                     None
WADMPR                                                  LAMPUNG
WIADPR                                                     None
TIPADM                                                        0
Shape_Leng                                             1.276866
Shape_Area                                             0.031223
geometry      POLYGON Z ((105.18844153600013 -5.430357782999...
Name: 10, dtype: object


#mengetahui luas area terkecil
bandarlampung.loc[bandarlampung['Shape_Area'].idxmin()]#luas area terkecil
#disini terlihat padang cermin karena memiliki 3 wilayah, dan satu dari wilayahnya merupakan luasan terkecil

KDPPUM                                                     None
NAMOBJ                                             PADANGCERMIN
REMARK                           Wilayah Administrasi Kecamatan
KDPBPS                                                     None
FCODE                                                      None
LUASWH                                                      0.0
UUPP                                                       None
SRS_ID                                                     None
LCODE                                                    BA0080
METADATA                                                   None
KDEBPS                                                     None
KDEPUM                                                     None
KDCBPS                                                     None
KDCPUM                                                     None
KDBBPS                                                     None
KDBPUM                                                     None
WADMKD                                                     None
WIADKD                                                     None
WADMKC                                                     None
WIADKC                                                     None
WADMKK                                                PESAWARAN
WIADKK                                                     None
WADMPR                                                  LAMPUNG
WIADPR                                                     None
TIPADM                                                        0
Shape_Leng                                              0.00507
Shape_Area                                             0.000001
geometry      POLYGON Z ((105.26072472300005 -5.489001772999...
Name: 1, dtype: object


#plotting luas wilayah dengan matplotlib scatterplot
fig, ax = plt.subplots(figsize=(14,6))
ax.scatter(bandarlampung['NAMOBJ'],bandarlampung['Shape_Area'])
ax.set_xlabel('Kecamatan')
plt.xticks(rotation='vertical')
ax.set_ylabel('Luas Wilayah')

Text(0, 0.5, 'Luas Wilayah')


#check missing value
print('Check kolom yang memiliki missing data:')
print(bandarlampung.isnull().any())

Check kolom yang memiliki missing data:
KDPPUM         True
NAMOBJ        False
REMARK        False
KDPBPS         True
FCODE          True
LUASWH        False
UUPP           True
SRS_ID         True
LCODE         False
METADATA       True
KDEBPS         True
KDEPUM         True
KDCBPS         True
KDCPUM         True
KDBBPS         True
KDBPUM         True
WADMKD         True
WIADKD         True
WADMKC         True
WIADKC         True
WADMKK        False
WIADKK         True
WADMPR        False
WIADPR         True
TIPADM        False
Shape_Leng    False
Shape_Area    False
geometry      False
dtype: bool


#Handling missing value by imputation
print("Before imputation: \n")
#Checking amount of missing value
print(bandarlampung.isnull().sum())

#Counting missing values
print(bandarlampung.isnull().sum().sum())

print('Ukuran bandarlampung sebelum di drop: \n', bandarlampung.shape)
print('\n Mengembalikan Keys dari list: \n', list(bandarlampung.keys()))

Before imputation: 

KDPPUM        31
NAMOBJ         0
REMARK         0
KDPBPS        31
FCODE         31
LUASWH         0
UUPP          31
SRS_ID        31
LCODE          0
METADATA      31
KDEBPS        31
KDEPUM        31
KDCBPS        31
KDCPUM        31
KDBBPS        31
KDBPUM        31
WADMKD        31
WIADKD        31
WADMKC        31
WIADKC        31
WADMKK         0
WIADKK        31
WADMPR         0
WIADPR        31
TIPADM         0
Shape_Leng     0
Shape_Area     0
geometry       0
dtype: int64
558
Ukuran bandarlampung sebelum di drop: 
 (31, 28)

 Mengembalikan Keys dari list: 
 ['KDPPUM', 'NAMOBJ', 'REMARK', 'KDPBPS', 'FCODE', 'LUASWH', 'UUPP', 'SRS_ID', 'LCODE', 'METADATA', 'KDEBPS', 'KDEPUM', 'KDCBPS', 'KDCPUM', 'KDBBPS', 'KDBPUM', 'WADMKD', 'WIADKD', 'WADMKC', 'WIADKC', 'WADMKK', 'WIADKK', 'WADMPR', 'WIADPR', 'TIPADM', 'Shape_Leng', 'Shape_Area', 'geometry']


#Handling missing value by dropping
print("After Dropping:")
#Drop non numerical object with missing value
bandarlampung = bandarlampung.dropna(axis='columns')#menghapus missing value pada columns

print('\n Mengembalikan Keys dari list setelah didrop:\n', list(bandarlampung.keys()))

print('\n Ukuran bandarlampung setelah didrop:', bandarlampung.shape)
bandarlampung.head()

After Dropping:

 Mengembalikan Keys dari list setelah didrop:
 ['NAMOBJ', 'REMARK', 'LUASWH', 'LCODE', 'WADMKK', 'WADMPR', 'TIPADM', 'Shape_Leng', 'Shape_Area', 'geometry']

 Ukuran bandarlampung setelah didrop: (31, 10)


#Cek Korelasi Data
print('Korelasi Shape_Leng dengan Shape_Area')
print(bandarlampung[['Shape_Leng', 'Shape_Area']].corr())

Korelasi Shape_Leng dengan Shape_Area
            Shape_Leng  Shape_Area
Shape_Leng      1.0000      0.9638
Shape_Area      0.9638      1.0000


#cek koordinat shp
bandarlampung.crs

<Compound CRS: EPSG:9518>
Name: WGS 84 + EGM2008 height
Axis Info [ellipsoidal|vertical]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
- H[up]: Gravity-related height (metre)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich
Sub CRS:
- WGS 84
- EGM2008 height


#ubah sistem koordinat referensi
bandarlampung = bandarlampung.to_crs(epsg=4326)
bandarlampung.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich


#plot pada peta
bandarlampung.plot()

<AxesSubplot:>


#menambahkan parameter cmap(color map)
bandarlampung.plot(cmap='jet')

<AxesSubplot:>


#parameter column untuk kategori pewarnaan, figsize untuk ukuran plot
bandarlampung.plot(cmap='jet', column = 'NAMOBJ', figsize = (10,10))

<AxesSubplot:>


#convert save shp ke GeoJSON
bandarlampung.to_file('Geospatial/bandarlampung/ADMINISTRASIKECAMATAN_AR_50K.geojson', driver='GeoJSON')

#open file geoJSON
bdl_geojson = r'Geospatial/bandarlampung/ADMINISTRASIKECAMATAN_AR_50K.geojson'
#buat gdf dari file GeoJSON
bdl_geogdf = gpd.read_file(bdl_geojson, driver='GeoJSON')

#open file json
bdl_json = r'Geospatial/bandarlampung/ADMINISTRASIKECAMATAN_AR_50K.json'
#buat gdf dari file json
bdl_gdf = gpd.read_file(bdl_json, driver='TopoJSON')

#Prepare GeoDataFrame for Altair

#dump as json
bdl_gdf_json = bdl_gdf.to_json()
# load as a GeoJSON object.
bdl_json_features = json.loads(bdl_gdf_json)

#parse variable `features` from json_features to `alt.Data`
bdl_data_geo = alt.Data(values=bdl_json_features['features'])


#Next we grab the states and put them in a list and check the length.
bdl_geogdf_list = list(bdl_geogdf.NAMOBJ.values)
len(bdl_geogdf_list)

31


bdl_geogdf.info()

<class 'geopandas.geodataframe.GeoDataFrame'>
RangeIndex: 31 entries, 0 to 30
Data columns (total 10 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   NAMOBJ      31 non-null     object  
 1   REMARK      31 non-null     object  
 2   LUASWH      31 non-null     float64 
 3   LCODE       31 non-null     object  
 4   WADMKK      31 non-null     object  
 5   WADMPR      31 non-null     object  
 6   TIPADM      31 non-null     int64   
 7   Shape_Leng  31 non-null     float64 
 8   Shape_Area  31 non-null     float64 
 9   geometry    31 non-null     geometry
dtypes: float64(3), geometry(1), int64(1), object(5)
memory usage: 2.5+ KB


# plot map, where variables ares nested within `properties`, 
alt.Chart(bdl_data_geo).mark_bar(
    fill='#023e8a',
    stroke='ffffff'
).properties(
    projection={'type': 'albersUsa'},
    width=500,
    height=225
).encode(
    alt.Y('properties.Shape_Area', type='quantitative'),
    alt.X('properties.NAMOBJ', type='nominal'),
    color=('properties.Shape_Area:Q')
)


# plot map, where variables ares nested within `properties`, 
alt.Chart(bdl_data_geo).mark_circle(
    fill='#023e8a',
    stroke='ffffff',
    size=60
).properties(
    projection={'type': 'albersUsa'},
    width=500,
    height=225
).encode(
    alt.Y('properties.Shape_Area', type='quantitative'),
    alt.X('properties.NAMOBJ', type='nominal'),
    color=('properties.Shape_Area:Q'),
    tooltip=['properties.NAMOBJ:nominal','properties.Shape_Area:Q']
).interactive()


# plot map, where variables ares nested within `properties`, 
grafik = alt.Chart(bdl_data_geo).mark_point().properties(
    width=500,
    height=225
).encode(
    x='properties.NAMOBJ:N',
    y='properties.Shape_Area:Q',
    tooltip=['properties.NAMOBJ:nominal','properties.Shape_Area:Q']
).interactive()

alt.vconcat(
    grafik.encode(color='properties.Shape_Area:N').properties(title='Nominal'),
)


#Buat peta dan marker graphic
from altair import Chart, load_dataset
import branca

#buat map dasar
m = folium.Map(location=[-5.443251942283236, 105.27673629553274],
                    zoom_start=10,
                    tiles = 'Stamen Color',
                    attr = 'Esri',
                    name = 'Esri Satellite',
                    control_scale = True
                    ) 



#buat style function

    


#load GeoJSON
folium.GeoJson(
    bdl_json_features,
    zoom_on_click=True,
    style_function=lambda feature: {
        "fillColor": "feature.properties.NAMOBJ",
        "color": "black",
        "weight": 2,
        "dashArray": "5, 5",
    },
    name="Load GeoJSON",
    ).add_to(m)

folium.Choropleth(
    bdl_geogdf,
    data=bdl_geogdf,
    columns=['NAMOBJ',"Shape_Area"], #Pembagian Berdasarkan Warna dan Legend
    key_on="feature.properties.NAMOBJ",
    bins=6,
    fill_color='YlGnBu',
    nan_fill_color='White',
    fill_opacity=1,
    nan_fill_opacity=None,
    line_color='Black',
    line_weight=1,
    line_opacity=0.4,
    name="Administrasi Bandarlampung",
    tooltip=['NAMOBJ','Shape_Area'],
    legend_name='Luas Area',
    overlay=True,
    control=True,
    show=True,
    topojson=None,
    smooth_factor=0,
    highlight=True
).add_to(m)

#buat grafik
scatter = alt.Chart(bdl_data_geo).mark_bar(
    fill='lightgray',
    stroke='white'
).properties(
    projection={'type': 'albersUsa'},
    width=300,
    height=150
).encode(
    alt.Y('properties.Shape_Area:Q'),
    alt.X('properties.NAMOBJ:N'),
    alt.Color('properties.Shape_Area:Q')
)

vega = folium.features.VegaLite(
    scatter,
    width="100%",
    height="100%",
)

#buat marker
marker = folium.features.Marker([-5.443251942283236, 105.27673629553274]) #buat marker

popup = folium.Popup() #buat wadah popup

vega.add_to(popup) #tambahkan chart vega ke popup
popup.add_to(marker) #tambahkan popup ke marker

marker.add_to(m)

m.add_child(MeasureControl(position="topleft"))
#membuat minimap menjadi fullscreen
plugins.Fullscreen(position='topleft').add_to(m)

#menggambar/interaktif map
draw = plugins.Draw(
  export=True,
  position="topleft"
)
draw.add_to(m)

#untuk menampilkan lonlang
m.add_child(folium.LatLngPopup())

plugins.LocateControl().add_to(m)

# We add a layer controller. 
folium.LayerControl(collapsed=True).add_to(m)

m


#buka shp bandarlampung untuk overlay
area_of_interest = gpd.read_file('Geospatial/bandarlampung/areainterest/area_of_interest.shp')
area_of_interest.head()


area_of_interest.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich


area_of_interest.plot()

<AxesSubplot:>


# color without fill, edgecolor = outline
cmap='hsv'
fig, ax = plt.subplots(figsize = (10,10))
bandarlampung.plot(ax = ax, cmap = cmap, edgecolor = 'black', column = 'NAMOBJ', legend=True)

#create Title
plt.title(f'Area of Interest Bandar Lampung: {cmap}')

#plot overlay kecamatan wayhalim dengan warna putih
area_of_interest.plot(ax = ax, color = 'none', edgecolor = 'white', linewidth=3)

<AxesSubplot:title={'center':'Area of Interest Bandar Lampung: hsv'}>


# Intersecting Layers
bandarlampung_in_aoi = gpd.overlay(bandarlampung, area_of_interest, how = 'intersection', keep_geom_type=True)
bandarlampung_in_aoi.plot(edgecolor = 'red', figsize=(8,8))
bandarlampung_in_aoi.head()


# geometry as boundary
bandarlampung_in_aoi.geometry.name

'geometry'


#buat column didalam bandarlampung_in_aoi
bandarlampung_in_aoi['centroid_column'] = bandarlampung_in_aoi.centroid

bdl_bulat = bandarlampung_in_aoi.set_geometry('centroid_column')

bdl_bulat.plot()

C:\Users\GIANAM~1\AppData\Local\Temp/ipykernel_26976/3736743472.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.

  bandarlampung_in_aoi['centroid_column'] = bandarlampung_in_aoi.centroid

<AxesSubplot:>


# select area BUMIWARAS
bandarlampung_in_aoi_bw=bandarlampung_in_aoi[bandarlampung_in_aoi['NAMOBJ'] == 'BUMIWARAS']

# explore as map
bandarlampung_in_aoi_bw.explore(column='NAMOBJ',
                                cmap='Set2',
                                legend=False,
                                tooltip=['NAMOBJ','Shape_Area'])


# explore as overlay map

m = bandarlampung.explore(
    column='NAMOBJ',
    name="Administrasi Bandar Lampung",
    cmap='Set2',
    #legend=False,
    tooltip=['NAMOBJ','Shape_Area'],
    #tiles='Stamen Terrain',
    style_kwds=dict(color="white",weight=2)
)

#buat overlay point of interest
bdl_bulat.explore(
     m=m, # pass the map object
     color="red", # use red color on all points
     marker_kwds=dict(radius=10, fill=True), # make marker radius 10px with fill
     tooltip="NAMOBJ", # show "name" column in the tooltip
     tooltip_kwds=dict(labels=False), # do not show column label in the tooltip
     name="Area of Interests" # name of the layer in the map
)

folium.TileLayer('Stamen Toner', control=True).add_to(m)
folium.LayerControl().add_to(m)

#membuat minimap menjadi fullscreen
plugins.Fullscreen(position='topleft').add_to(m)

#menggambar/interaktif map
draw = plugins.Draw(
  export=True,
  position="topleft"
)
draw.add_to(m)

#untuk menampilkan lonlang
m.add_child(folium.LatLngPopup())


# check mark point
bdl_bulat.head()


from shapely.geometry import Point

# create marker manually
#buat dictionary for marker
d = {'name': ['Marker1'], 'geometry': [Point(105.27673629553274,-5.443251942283236)]}

#buat GeoDataFrame
marker_gdf = gpd.GeoDataFrame(d, crs="EPSG:4326") #buat marker dari gdf

marker_gdf

#convert to json
#marker_json = marker_gdf.to_json()


# create overlay map using explore 
mo = folium.Map(location=[-5.443251942283236, 105.27673629553274],
                    zoom_start=12,
                    tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
                    attr = 'Esri',
                    name = 'Esri Satellite',
                    control_scale = True
                    )

m = bandarlampung.explore(
    column='NAMOBJ',
    name="Administrasi Bandar Lampung",
    cmap='Set2',
    #legend=False,
    tooltip=['NAMOBJ','Shape_Area'],
    #tiles='Stamen Terrain',
    style_kwds=dict(color="white",weight=2)
)


#Create blue marker
marker_gdf.explore(
    m=m,
    color="blue",
    marker_kwds=dict(radius=5, fill=True), # make marker radius 10px with fill
    tooltip="name", # show "name" column in the tooltip
    tooltip_kwds=dict(labels=False), # do not show column label in the tooltip
    name="Area of Int" # name of the layer in the map
)
    
#buat overlay point of interest
bdl_bulat.explore(
     m=m, # pass the map object
     color="red", # use red color on all points
     marker_kwds=dict(radius=10, fill=True), # make marker radius 10px with fill
     tooltip="NAMOBJ", # show "name" column in the tooltip
     tooltip_kwds=dict(labels=False), # do not show column label in the tooltip
     name="Area of Interests" # name of the layer in the map
)

folium.TileLayer('Stamen Toner', control=True).add_to(m)
folium.LayerControl().add_to(m)

#membuat minimap menjadi fullscreen
plugins.Fullscreen(position='topleft').add_to(m)

#menggambar/interaktif map
draw = plugins.Draw(
  export=True,
  position="topleft"
)
draw.add_to(m)

#untuk menampilkan lonlang
m.add_child(folium.LatLngPopup())


bandarlampung_in_aoi.explore(column='NAMOBJ',
                                cmap='Set2',
                                legend=False,
                                tooltip=['NAMOBJ','Shape_Area'],
                                tiles='Stamen Terrain',
                                style_kwds=dict(color="white",weight=2)
                            )


# create folium map
m2 = folium.Map(location=[-5.443251942283236, 105.27673629553274],
                    zoom_start=12,
                    tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
                    attr = 'Esri',
                    name = 'Esri Satellite',
                    control_scale = True
                    )



#Create Measure Control
m2.add_child(MeasureControl(position="topright"))

#membuat minimap menjadi fullscreen
plugins.Fullscreen(position='topleft').add_to(m2)

#menggambar/interaktif map
draw = plugins.Draw(
  export=True,
  position="topleft"
)
draw.add_to(m2)

#untuk menampilkan lonlang
m2.add_child(folium.LatLngPopup())

m2


#Create Choropleth Map
import pandas as pd
folium.Choropleth(
    geo_data=bandarlampung,
    name="choropleth",
    data=bandarlampung,
    columns=["NAMOBJ", "Shape_Area"],
    key_on="feature.id",
    fill_color="YlGnBu",
    fill_opacity=0.7,
    line_color = "white",
    line_opacity=0.2,
    legend_name="Luas Area",
).add_to(m)

m

	KDPPUM	NAMOBJ	REMARK	KDPBPS	FCODE	UUPP	SRS_ID	LCODE	METADATA	...	WADMKC	WIADKC	WADMKK	WIADKK	WADMPR	WIADPR	Shape_Leng	Shape_Area	geometry
0	None	PADANGCERMIN	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	PESAWARAN	None	LAMPUNG	None	0.015246	9.276147e-06	POLYGON Z ((105.27223 -5.51068 0.00000, 105.27...
1	None	PADANGCERMIN	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	PESAWARAN	None	LAMPUNG	None	0.005070	9.054481e-07	POLYGON Z ((105.26072 -5.48900 0.00000, 105.26...
2	None	TELUKBETUNG TIMUR	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	BANDARLAMPUNG	None	LAMPUNG	None	0.014730	9.877191e-06	POLYGON Z ((105.26501 -5.46243 0.00000, 105.26...
3	None	KATIBUNG	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	LAMPUNG SELATAN	None	LAMPUNG	None	0.959100	1.292074e-02	MULTIPOLYGON Z (((105.34679 -5.52032 0.00000, ...
4	None	TELUKBETUNG TIMUR	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	BANDARLAMPUNG	None	LAMPUNG	None	0.173407	8.594191e-04	POLYGON Z ((105.26360 -5.45600 0.00000, 105.26...

	KDPPUM	NAMOBJ	REMARK	KDPBPS	FCODE	UUPP	SRS_ID	LCODE	METADATA	...	WADMKC	WIADKC	WADMKK	WIADKK	WADMPR	WIADPR	Shape_Leng	Shape_Area	geometry
26	None	GEDONGTATAAN	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	PESAWARAN	None	LAMPUNG	None	0.617663	0.012691	MULTIPOLYGON Z (((105.20690 -5.38728 0.00000, ...
27	None	RAJABASA	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	BANDARLAMPUNG	None	LAMPUNG	None	0.222881	0.001053	POLYGON Z ((105.26089 -5.32809 0.00000, 105.26...
28	None	TANJUNGBINTANG	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	LAMPUNG SELATAN	None	LAMPUNG	None	0.752524	0.013876	POLYGON Z ((105.34452 -5.43371 0.00000, 105.34...
29	None	JATIAGUNG	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	LAMPUNG SELATAN	None	LAMPUNG	None	1.047365	0.019409	MULTIPOLYGON Z (((105.29253 -5.35083 0.00000, ...
30	None	NATAR	Wilayah Administrasi Kecamatan	None	None	None	None	BA0080	None	...	None	None	LAMPUNG SELATAN	None	LAMPUNG	None	1.051700	0.021527	MULTIPOLYGON Z (((105.24889 -5.33292 0.00000, ...

	LUASWH	TIPADM	Shape_Leng	Shape_Area
count	31.0	31.0	31.000000	3.100000e+01
mean	0.0	0.0	0.337638	4.720350e-03
std	0.0	0.0	0.357225	7.867549e-03
min	0.0	0.0	0.005070	9.054481e-07
25%	0.0	0.0	0.115021	3.317498e-04
50%	0.0	0.0	0.164935	8.594191e-04
75%	0.0	0.0	0.483822	5.508638e-03
max	0.0	0.0	1.276866	3.122260e-02